{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_color_identity`\n",
    "`scale_color_identity` applies the color values in your data to your ggplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>ze-color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>MediumAquaMarine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Tomato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>#f8b195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>#ffb6c1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>#933835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Bisque</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y          ze-color\n",
       "0  0  0              blue\n",
       "1  1  1               red\n",
       "2  2  2             green\n",
       "3  3  3  MediumAquaMarine\n",
       "4  4  4              Peru\n",
       "5  5  5            Tomato\n",
       "6  6  6           #f8b195\n",
       "7  7  7           #ffb6c1\n",
       "8  8  8           #933835\n",
       "9  9  9            Bisque"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ze_colors = [\n",
    "    \"blue\",\n",
    "    \"red\",\n",
    "    \"green\",\n",
    "    \"MediumAquaMarine\",\n",
    "    \"Peru\",\n",
    "    \"Tomato\",\n",
    "    \"#f8b195\",\n",
    "    \"#ffb6c1\",\n",
    "    \"#933835\",\n",
    "    \"Bisque\"\n",
    "]\n",
    "df = pd.DataFrame({\n",
    "        \"x\": range(10),\n",
    "        \"y\": range(10),\n",
    "        \"ze-color\": ze_colors\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EpBgZY+ja1Yypa6J8e4OnPQs95doW9SHDurdX8ZfHH8W4/rtjDxDr358J35zNsDHD6N+H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\nRERERAGxNBARERERUUD/H4p+Qy9dYbBFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe69650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285108485)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df, aes(x='x', y='y', color='ze-color')) + \\\n",
    "    geom_point(size=7500) + \\\n",
    "    scale_color_identity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
